Deep learning and quantum-enhanced predictive optimization in power electronics

Abstract

Optimization plays a crucial part in the plan, control, and operation of modern power electronic systems. Traditional methods, viz. Genetic Algorithm, Particle Swarm Optimization (PSO), and Differential Evolution have been widely used to optimize converter efficiency, stability, and performance. However, the increasing complexity of renewable energy systems, electric vehicles, and smart grids necessitate advanced optimization frameworks. This paper discovers the incorporation of Artificial Intelligence, Machine Learning, and Quantum Machine Learning into power electronics optimization. Reinforcement Learning is investigated for adaptive control of converters and motor drives, while Neural Networks are explored for predictive control. Hybrid optimization methods, viz Fuzzy with PSO and Artificial Neural Networks with Genetic Algorithm, are presented to improve convergence speed and accuracy. Simulation platforms, like MATLAB and Python are leveraged to evaluate optimization frameworks. Finally, we introduce a novel deep learning-based predictive controller augmented with QML techniques for converters in EV and renewable systems. We propose a DL and QML-based predictive controller that attains around 15 % lower converter losses compared to classical methods.